The dimensions of data appear appropriate. Inspecting the structure of each variable, it appears that they are appropriately placed as characters, integers, and numerical values.
Step 3. Format the data
# format the datecv_states$date <-as.Date(cv_states$date, format="%Y-%m-%d")# format the state and state abbreviation (abb) variablesstate_list <-unique(cv_states$state)cv_states$state <-factor(cv_states$state, levels = state_list)abb_list <-unique(cv_states$abb)cv_states$abb <-factor(cv_states$abb, levels = abb_list)
# order the data first by state, second by datecv_states = cv_states[order(cv_states$state, cv_states$date)]
# Confirm the variables are now correctly formattedstr(cv_states)
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?head(cv_states)
state date fips cases deaths geo_id population pop_density abb
1: Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
2: Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
3: Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
4: Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
5: Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
6: Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
summary(cv_states)
state date fips cases
Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1
Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125
California : 1154 Median :2021-09-11 Median :29.00 Median : 418120
Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941
Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318
Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158
(Other) :51184
deaths geo_id population pop_density
Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292
1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659
Median : 5901 Median :29.00 Median : 4468402 Median : 107.860
Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031
3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511
Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120
NA's :1106
abb
WA : 1158
IL : 1155
CA : 1154
AZ : 1153
MA : 1147
WI : 1143
(Other):51184
min(cv_states$date)
[1] "2020-01-21"
max(cv_states$date)
[1] "2023-03-23"
The date range runs from 1/21/2020 to 3/23/2023. The range of cases were 1 - 12,169,158. The range of deaths were 0 - 104,277.
Step 4. Add new_cases and new_deaths
# Add variables for new_cases and new_deaths:for (i in1:length(state_list)) { cv_subset =subset(cv_states, state == state_list[i]) cv_subset = cv_subset[order(cv_subset$date),]# add starting level for new cases and deaths cv_subset$new_cases = cv_subset$cases[1] cv_subset$new_deaths = cv_subset$deaths[1] for (j in2:nrow(cv_subset)) { cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j -1] cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j -1]}# include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths}
# Focus on recent datescv_states <- cv_states %>% dplyr::filter(date >="2021-06-01")
# Inspect outliers in new_cases using plotlyp1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) +geom_line() +geom_point(size = .5, alpha =0.5) p1a <-ggplotly(p1)p1<-NULL# to clear from workspacep1a
Upon initial inspection, we can see that there are both positive and negative outliers. Of note, the following states have negative outliers of new cases: Florida, Tenesse, Kentucky, Pennsylvania, Washington, California, Delaware, to name a few (it was freezing and draining my laptop battery to continute looking at more).
p2 <-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) +geom_boxplot() +geom_point(size = .5, alpha =0.5)p2a <-ggplotly(p2)p2a
p2<-NULL# to clear from workspace
Upon initial inspection, we can see that there are both positive and negative outliers. Of note, the following states have negative outliers of new deaths: Colorado, Massachusetts, California, to name a few (it was freezing and draining my laptop battery to continute looking at more).
# set negative new case or death counts to 0cv_states$new_cases[cv_states$new_cases<0] =0cv_states$new_deaths[cv_states$new_deaths<0] =0
# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`for (i in1:length(state_list)) { cv_subset =subset(cv_states, state == state_list[i])# add starting level for new cases and deaths cv_subset$cases = cv_subset$cases[1] cv_subset$deaths = cv_subset$deaths[1]### FINISH CODE HEREfor (j in2:nrow(cv_subset)) { cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j -1] cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j -1] }# include in main dataset cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths}
# Smooth new countscv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>%round(digits =0)cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>%round(digits =0)# Inspect data again interactivelyp2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) +geom_line() +geom_point(size = .5, alpha =0.5)p2b <-ggplotly(p2)p2 <-NULL
Outliers or values that don’t make sense for new cases and new deaths:
table(is.na(cv_states))
FALSE TRUE
377419 673
Step 5. Add additional variables
### FINISH CODE HERE# add population normalized (by 100,000) counts for each variablecv_states$per100k =as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))cv_states$newper100k =as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / casescv_states = cv_states %>%mutate(naive_CFR =round((deaths*100/cases),2))# create a `cv_states_today` variablecv_states_today =subset(cv_states, date==max(cv_states$date))
Step 6. Explore scatterplots using plot_ly
# pop_density vs. casescv_states_today %>%plot_ly(x =~pop_density, y =~cases, type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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Returning the palette you asked for with that many colors
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# filter out "District of Columbia"cv_states_today_filter <- cv_states_today %>%filter(state!="District of Columbia")
# pop_density vs. cases after filteringcv_states_today_filter %>%plot_ly(x =~pop_density, y =~cases, type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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# pop_density vs. deathsper100kcv_states_today_filter %>%plot_ly(x =~pop_density, y =~deathsper100k,type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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# Adding hoverinfocv_states_today_filter %>%plot_ly(x =~pop_density, y =~deathsper100k,type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5),hoverinfo ='text',text =~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , paste(" Deaths per 100k: ", deathsper100k, sep=""), sep ="<br>")) %>%layout(title ="Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",yaxis =list(title ="Deaths per 100k"), xaxis =list(title ="Population Density"),hovermode ="compare")
Warning: Ignoring 1 observations
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Step 7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()
p3 <-ggplot(cv_states_today, aes(x = pop_density, y = newdeathsper100k, size = population)) +geom_point() +geom_smooth() +labs(title ="Scatterplot of pop_density vs. newdeathsper100k",x ="Population Density",y ="New deaths per 100k")ggplotly(p3)
`geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Warning: The following aesthetics were dropped during statistical transformation: size
ℹ This can happen when ggplot fails to infer the correct grouping structure in
the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
variable into a factor?
Based on this plot alone, there does not appear to be a correlation between new deaths per 100k. The correlation and linear regression line does not appear to have a direct relationship.
Step 8. Multiple Line Chart
# Line chart for naive_CFR for all states over time using `plot_ly()`plot_ly(cv_states, x =~date, y =~naive_CFR, color =~state, type ="scatter", mode ="lines")
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# Line chart for Florida showing new_cases and new_deaths togethercv_states %>%filter(state=="Florida") %>%plot_ly(x =~date, y =~new_cases, type ="scatter", mode ="lines") %>%add_trace(x =~date, y =~new_deaths, type ="scatter", mode ="lines")
Overtime those that had an increase in CFR in September eventually leveled out and decreased again. The drop was significant in December 2022/January 2023.
What is the time delay between the peak of cases and the peak of deaths? While it is not perfectly clear what the time delay is, after zooming in we can estimate that it is about: January 10th (peak of cases) to February 16th (peak of deaths) which was approximately over a month: almost 5 weeks.
After adjusting for per100k, the states that stand out include: Rhode Island, Utah, Wisconsin, New Mexico, and New Jersey.
# Create a second heatmap after filtering to only include dates every other weekfilter_dates <-seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by=14)cv_states_mat <- cv_states %>%select(state, date, newper100k) %>%filter(date %in% filter_dates)cv_states_mat2 <-as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))rownames(cv_states_mat2) <- cv_states_mat2$datecv_states_mat2$date <-NULLcv_states_mat2 <-as.matrix(cv_states_mat2)# Create a heatmap using plot_ly()plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),z=~cv_states_mat2,type="heatmap",showscale=T)
Step 10. Map
### For specified datepick.date ="2021-10-15"# Extract the data for each state by its abbreviationcv_per100 <- cv_states %>%filter(date==pick.date) %>%select(state, abb, newper100k, cases, deaths) # select datacv_per100$state_name <- cv_per100$statecv_per100$state <- cv_per100$abbcv_per100$abb <-NULL# Create hover textcv_per100$hover <-with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))# Set up mapping detailsset_map_details <-list(scope ='usa',projection =list(type ='albers usa'),showlakes =TRUE,lakecolor =toRGB('white'))# Make sure both maps are on the same color scaleshadeLimit <-125# Create the mapfig <-plot_geo(cv_per100, locationmode ='USA-states') %>%add_trace(z =~newper100k, text =~hover, locations =~state,color =~newper100k, colors ='Purples' )fig <- fig %>%colorbar(title =paste0("Cases per 100k: ", pick.date), limits =c(0,shadeLimit))fig <- fig %>%layout(title =paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),geo = set_map_details )fig_pick.date <- fig################ Map for today's date# Extract the data for each state by its abbreviationcv_per100 <- cv_states_today %>%select(state, abb, newper100k, cases, deaths) # select datacv_per100$state_name <- cv_per100$statecv_per100$state <- cv_per100$abbcv_per100$abb <-NULL# Create hover textcv_per100$hover <-with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))# Set up mapping detailsset_map_details <-list(scope ='usa',projection =list(type ='albers usa'),showlakes =TRUE,lakecolor =toRGB('white'))# Create the mapfig <-plot_geo(cv_per100, locationmode ='USA-states') %>%add_trace(z =~newper100k, text =~hover, locations =~state,color =~newper100k, colors ='Purples' )fig <- fig %>%colorbar(title =paste0("Cases per 100k: ", Sys.Date()), limits =c(0,shadeLimit))fig <- fig %>%layout(title =paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),geo = set_map_details )fig_Today <- fig### Plot together subplot(fig_pick.date, fig_Today, nrows =2, margin = .05)